63 lines
1.4 KiB
YAML
63 lines
1.4 KiB
YAML
# Gemma 4 E2B Vision LoRA
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#
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# Fine-tuning LM LoRA adapters on multimodal Gemma4 with vision/multimodal modules frozen.
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# Uses the base ProcessingStrategy (auto-detects image_token from processor).
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base_model: google/gemma-4-E2B-it
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processor_type: AutoProcessor
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freeze_mm_modules: true
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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strict: false
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# Required for vision/multimodal training
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skip_prepare_dataset: true
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remove_unused_columns: false
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sample_packing: false
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chat_template: gemma4
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datasets:
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- path: HuggingFaceH4/llava-instruct-mix-vsft
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type: chat_template
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split: train[:100]
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val_set_size: 0
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output_dir: ./outputs/gemma4-e2b-vision-lora
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adapter: lora
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sequence_len: 2048
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pad_to_sequence_len: false
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0
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# Target language model only — vision encoder is frozen via freeze_mm_modules
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lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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num_epochs: 1
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max_steps: 10
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optimizer: adamw_torch_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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logging_steps: 1
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sdp_attention: true
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warmup_ratio: 0.1
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weight_decay: 0.0
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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